Dear　Editor,　This　letter　proposes　a　contrastive　consensus　graph　learning　model　for　multi-view　clustering.　Graphs　are　usually　built　to　outline　the　correlation　between　multi-model　objects　in　clustering　task,　and　multiview　graph　clustering　aims　to　learn　a　consensus　graph　that　integrates　the　spatial　property　of　each　view.　Nevertheless,　most　graph-based　models　merely　consider　the　overall　structure　from　all　views　but　neglect　the　local　spatial　consistency　between　diverse　views,　resulting　in　the　lack　of　global　spatial　consistency　in　the　learned　graph.　To　overcome　this　issue,　a　deep　convolutional　network　is　built　to　explore　latent　local　spatial　information　from　raw　affinity　graphs.　Specifically,　we　employ　a　consensus　graph　constraint　to　preserve　the　global　consistency　between　the　learned　graph　and　raw　graphs.　Furthermore,　a　contrastive　reconstruction　loss　is　introduced　to　achieve　the　sample-level　approximation　between　reconstructed　graphs　and　raw　graphs,　which　facilitates　the　network　to　enhance　the　consensus　graph　learning.　Experiments　on　six　classical　datasets　demonstrate　that　the　proposed　model　outperforms　other　nine　state-of-the-art　algorithms.　©　2014　Chinese　Association　of　Automation.